Please use this identifier to cite or link to this item:
http://hdl.handle.net/1942/30365
Title: | Comparison of different software implementations for spatial disease mapping | Authors: | VRANCKX, Maren NEYENS, Thomas FAES, Christel |
Issue Date: | 2019 | Publisher: | ELSEVIER SCI LTD | Source: | Spatial and spatio-temporal epidemiology, 31 (Art N° 100302) | Abstract: | Disease mapping is a scientific field that aims to understand and predict disease risk based on counts of observed cases within small regions of a study area of interest. Hierarchical model-based approaches that borrow information from neighbouring areas via conditional autoregressive (CAR) random effects on the local disease rates have gained a lot of popularity, thanks to the readily implemented Markov chain Monte Carlo methods. Nowadays, many software implementations to model risk distributions exist. Many of these applications differ, to varying degrees, in the underlying methodology. This paper provides an in-depth comparison between analysis results, coming from R-packages CARBayes, R2OpenBUGS, NIMBLE, R2BayesX, R-INLA, and RStan. We investigate CAR models typically used in disease mapping for spatially discrete count data. Data about diabetics in children and young adults in Belgium are used in a case study, while simulation studies are undertaken to assess software performance in different settings. (C) 2019 Elsevier Ltd. All rights reserved. | Keywords: | Disease mapping;Conditional autoregressive models;Software packages;Relative risks;Diabetics | Document URI: | http://hdl.handle.net/1942/30365 | ISSN: | 1877-5845 | e-ISSN: | 1877-5853 | DOI: | 10.1016/j.sste.2019.100302 | ISI #: | WOS:000496470600004 | Rights: | 2019 Elsevier Ltd. All rights reserved. | Category: | A1 | Type: | Journal Contribution | Validations: | vabb 2022 |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
1-s2.0-S1877584518301035-main.pdf Restricted Access | Published version | 10.78 MB | Unknown | View/Open Request a copy |
Manuscript.pdf | Peer-reviewed author version | 9.95 MB | Unknown | View/Open |
SCOPUSTM
Citations
1
checked on Sep 2, 2020
WEB OF SCIENCETM
Citations
7
checked on Apr 22, 2024
Page view(s)
100
checked on Sep 7, 2022
Download(s)
80
checked on Sep 7, 2022
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.